Obtain resources upon detection that more resources are needed for a workload

Designing for elasticity ensures that your resources scale dynamically based on current demand. This capability not only maintains performance but also minimizes costs by preventing over-provisioning. Understanding how to automatically adjust resources plays a crucial role in enhancing the reliability of your workload.

Best Practices

Implement Auto Scaling Policies

• Set up Auto Scaling groups for your compute resources to automatically adjust capacity based on demand. This is crucial for ensuring that your application can handle sudden traffic spikes without becoming overloaded and compromising availability.
• Use CloudWatch metrics to monitor key application performance indicators, such as CPU utilization, request counts, or latency. Configure alarms that trigger scaling actions when thresholds are breached, ensuring a proactive response to increased load.
• Regularly review and adjust your scaling policies based on historical usage patterns and forecasted demand, allowing for continuous improvement in your scalability strategy.

Leverage Serverless Architectures

• Consider using serverless services like AWS Lambda or Amazon DynamoDB that automatically handle scaling based on incoming requests. This approach abstracts the underlying infrastructure management, allowing you to focus on application functionality and reliability.
• Take advantage of built-in redundancy and failover capabilities provided by serverless services to enhance your application’s reliability without much added complexity.
• Monitor usage and optimize triggering events to manage costs effectively while maintaining scalability and reliability.

Design for Statelessness

• Ensure your application components are stateless to facilitate horizontal scaling. Stateless applications can be scaled out easily by adding more instances, which maintains availability and reliability during demand spikes.
• Utilize external storage solutions for state management, such as Amazon S3 or Amazon RDS, which allows your application to scale independently from its state management.
• Implement session management strategies like sticky sessions judiciously to avoid bottlenecks in your application during peak traffic.

Questions to ask your team

• How do you monitor your current resource utilization to detect when more resources are needed?
• What automated scaling solutions are you using to respond to changes in demand?
• How quickly can your system respond to increases in demand with added resources?
• Are there any thresholds set for triggering resource scaling, and how are they monitored?
• What strategies do you have in place for scaling down resources when demand decreases?

Who should be doing this?

Cloud Architect

• Design scalable architectures that can handle fluctuations in demand.
• Implement auto-scaling features in workloads to adjust resources based on real-time demand.
• Monitor application performance and resource utilization to identify scaling needs.
• Collaborate with development teams to ensure application readiness for dynamic scaling.

Site Reliability Engineer (SRE)

• Set up monitoring and alerting systems to detect changes in demand promptly.
• Analyze performance metrics to inform scaling decisions.
• Conduct capacity planning and testing to ensure the system can meet peak loads.
• Develop and maintain runbooks for emergency scaling actions.

DevOps Engineer

• Automate deployment processes to enable swift scaling.
• Integrate scaling solutions within CI/CD pipelines.
• Manage cloud resource configurations and policies for automatic scaling.
• Collaborate with Cloud Architects to implement best practices for resource management.

Product Owner

• Define business requirements regarding scaling capabilities.
• Ensure that scaling features align with user experience and service level agreements (SLAs).
• Prioritize enhancements related to reliability and scalability within the product backlog.

What evidence shows this is happening in your organization?

• Auto Scaling Strategy Template: A comprehensive template that outlines strategies for implementing AWS Auto Scaling, detailing the policies and triggers needed to scale resources automatically based on demand.
• Reliability Dashboard: A real-time dashboard that visualizes key performance metrics and resource utilization, enabling teams to monitor workload performance and adapt resource allocation proactively.
• Scalability Playbook: A playbook that provides guidelines and best practices for designing scalable architectures, including step-by-step instructions for configuring auto-scaling groups and load balancers.
• Incident Response Plan: A plan that outlines processes and protocols for responding to sudden demand spikes, ensuring that workloads can scale efficiently and maintain availability during peak times.
• Monitoring Checklist: A checklist that includes essential monitoring metrics and alerts necessary for detecting when additional resources are needed, ensuring proactive scaling to maintain workload performance.

Cloud Services

AWS

• Amazon EC2 Auto Scaling: Automatically adjusts the number of EC2 instances up or down based on demand to ensure sufficient capacity.
• Amazon Elastic Load Balancing: Distributes incoming application traffic across multiple targets, such as EC2 instances, and can scale based on current demand.
• AWS Lambda: Runs code in response to events and automatically scales the execution based on the request load.

Azure

• Azure Autoscale: Automatically adjusts resources, such as Virtual Machines and App Services, to match the traffic demand.
• Azure Load Balancer: Distributes incoming network traffic across several servers and adjusts dynamically to incoming requests.
• Azure Functions: Executes code in response to events and scales out based on demand in a serverless environment.

Google Cloud Platform

• Google Compute Engine Autoscaler: Dynamically adjusts the number of VM instances in a managed instance group in response to load conditions.
• Google Cloud Load Balancing: Distributes traffic across multiple instances and can automatically scale based on demand.
• Google Cloud Functions: Runs code in response to events and automatically scales based on the number of requests.

Question: How do you design your workload to adapt to changes in demand?
Pillar: Reliability (Code: REL)